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A sensor-based SLAM algorithm for camera tracking in virtual studio

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Abstract

This paper addresses a sensor-based simultaneous localization and mapping (SLAM) algorithm for camera tracking in a virtual studio environment. The traditional camera tracking methods in virtual studios are vision-based or sensor-based. However, the chroma keying process in virtual studios requires color cues, such as blue background, to segment foreground objects to be inserted into images and videos. Chroma keying limits the application of vision-based tracking methods in virtual studios since the background cannot provide enough feature information. Furthermore, the conventional sensor-based tracking approaches suffer from the jitter, drift or expensive computation due to the characteristics of individual sensor system. Therefore, the SLAM techniques from the mobile robot area are first investigated and adapted to the camera tracking area. Then, a sensor-based SLAM extension algorithm for two dimensional (2D) camera tracking in virtual studio is described. Also, a technique called map adjustment is proposed to increase the accuracy and efficiency of the algorithm. The feasibility and robustness of the algorithm is shown by experiments. The simulation results demonstrate that the sensor-based SLAM algorithm can satisfy the fundamental 2D camera tracking requirement in virtual studio environment.

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References

  1. K. Cornelis, M. Pollefeys, L. V. Gool. Tracking Based Structure and Motion Recovery for Augmented Vdeo Productions. In Proceedings of the ACM Symposium on Virtual Reality Software and Technology, ACM, Alberta, Canada, pp. 17–24. 2001.

    Chapter  Google Scholar 

  2. M. I. Lourakis, A. A. Argyros. Efficient, Causal Camera Tracking in Unprepared Environments. Computer Vision and Image Understanding, vol. 99, no. 2, pp. 259–290, 2005.

    Article  Google Scholar 

  3. M. A. Livngston, A. State. Magnetic Tracker Calibration for Improved Augmented Reality Registration. Teleoperators and Virtual Enviroment, vol. 6, no. 5, pp. 532–546, 1997.

    Google Scholar 

  4. F. Madritsch. Optical Beacon Tracking for Human-computer Interfaces, Ph. D. dissertation, Technical University Graz, Austria, 1996.

    Google Scholar 

  5. R. Want. A. Hopper. V. Falcao, I. Gibbons. The Active Badge Location System. ACM Transactions on Information Systems, vol. 10, no. 1, pp. 91–102, 1992.

    Article  Google Scholar 

  6. C. Randell, H. Muller. Low Cost Indoor Positioning System. Ubicomp 2001: Ubiquitous Computing, G. D. Abowd (ed.), Springer-Verlag, Berlin, Germany, pp. 42–48. 2001.

    Chapter  Google Scholar 

  7. S. Gibbs, C. Arapis, C. Breiteneder, V. Lalioti, S. Mostafaway, J. Speier. Virtual Studios: An Overview. IEEE Multimedia, vol. 5, no.1, pp. 18–35, 1998.

    Article  Google Scholar 

  8. A. Wojdala. Challenges of Virtual Set Technology. IEEE Multimedia, vol. 5, no. 1, pp. 50–57, 1998.

    Article  Google Scholar 

  9. Y. Yamanouchi, H. Mitsumine, T. Fukaya, M. Kawakita, N. Yagi, S. Inoue. Real Space-based Virtual Studio-Seamless Synthesis of a Real Set Image with a Virtual Set Image. In Proceedings of the ACM Symposium on Virtual Reality Software and Technology, ACM, HongKong, PRC, pp. 194–200, 2002.

    Chapter  Google Scholar 

  10. D. Scharstein, R. Szeliski. A Taxonomy and Evaluation of Dense Two-frame Stereo Correspondence Algorithms. International Journal of Computer Vision, vol. 47, no. 1–3, pp. 7–42, 2002.

    Article  MATH  Google Scholar 

  11. R. Yang, M. Pollefeys, H. Yang, G. Welch. A Unified Approach to Real-time, Multi-resolution, Multi-baseline 2D View Synthesis and 3D Depth Estimation Using Commodity and Graphics Hardware. International Journal of Image and Graphics, vol. 4, no. 4, pp. 627–651, 2004.

    Article  Google Scholar 

  12. A. Ward, A. Jones, A. Hopper. A New Location Technique for the Active Office. IEEE Personal Communications, vol. 4, no. 5, pp. 42–47, 1997.

    Article  Google Scholar 

  13. R. A. Brooks. A Robot That Walks: Emergent Behavior from a Carefully Evolved Network. Neural Computation, vol. 1, no. 2, pp. 253–262, 1989.

    Article  Google Scholar 

  14. S. Thrun, D. Fox, W, Burgard, F. Dellaert. Robust Monte Carlo Localization for Mobile Robots. Artificial Intelligence, vol. 128, no. 1–2, pp. 99–141. 2001.

    Article  MATH  Google Scholar 

  15. M. Pupilli, A. Calway. Real-time Camera Tracking Using a Particle Filter. In Proceedings of British Machine Vision Conference, Oxford, UK, pp. 519–528, 2005.

  16. A. J. Davison. Real-time Simultaneous Localisation and Mapping with a Single Camera. In Proceedings of IEEE International Conference on Computer and Vision, IEEE Press, Washington D.C., USA, vol. 2, pp. 1403–1410, 2003.

    Chapter  Google Scholar 

  17. A. J. Davison, D. W. Murray. Simultaneous Localization and Map-building Using Active Vision. IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 24, no. 7, pp. 865–880 2002.

    Article  Google Scholar 

  18. M. H. Degroot, M. J. Schervish. Probability and Statistics, 3rd ed., Addison-Wesley, USA, 2002.

    Google Scholar 

  19. A. Doucet, D. Freitas, K. Murphy, S. Russell. Rao-blackwellised Particle Filtering for Dynamic Bayesian Networks. In Proceedings of the 16th Conference on Uncertainty in Artificial Intelligence, Stanford, USA, pp. 176–183, 2000.

  20. M. C. Deans, M. Hebert. Experimental Comparison of Techniques for Localization and Mapping Using a Bearing-only Sensor. Lecture Notes in Control and Information Sciences, Experimental Robotics VII, Springer-Verlag, London, UK, vol. 271, pp. 395–404, 2002.

    Google Scholar 

  21. S. B. Williams, G. Dissanayake, H. Durrant-Whyte. Constrained Initialisation of the Simultaneous Localization and Mapping Algorithm. In Proceedings of International Conference on Field and Service Robotics, Helsinki, Finland, pp. 315–330, 2001.

  22. J. R. Spletzer. A New Approach to Range-only SLAM for Wireless Sensor Networks, Techniqucal Report, Lehigh University Bethlehem, PA, USA, 2003

    Google Scholar 

  23. J. J. Leonard, R. J. Rikoski. Incorporation of Delayed Decision Making into Stochastic Mapping. Lecture Notes in Control and Information Sciences, Springer-Verlag, Berlin, Germany, Volume 271, pp. 533–542, 2000.

    Google Scholar 

  24. J. M. Buhmann, W. Burgard, A. B. Cremers, D. Fox, T. Hofmann, F. E. Schneider, J. Strikos, S. Thrun. The Mobile Robot Rhino. AI Magazine, vol. 16, no. 2, pp. 31–38, 1995.

    Google Scholar 

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Authors and Affiliations

  1. Faculty of Computing, Engineering and Technology, Staffordshire University, Stafford, ST16 9DG, UK

    Po Yang, Wenyan Wu, Mansour Moniri & Claude C. Chibelushi

Authors
  1. Po Yang
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  2. Wenyan Wu
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  3. Mansour Moniri
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  4. Claude C. Chibelushi
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Corresponding author

Correspondence to Po Yang.

Additional information

Po Yang received the B. Sc. degree in computer science from the Wuhan University, China, in 2004, and the M. Sc. degree in computer science from Bristol University, UK, in 2006. Currently, he is a Ph.D. candidate at the Faculty of Computing, Engineering and Technology in Staffordshire University, UK. He has published about 4 refereed conference papers.

His research interests include image processing, computer vision, virtual reality, RFID, and sensor networking.

Wenyan Wu received the B. Sc. and M. Sc. degrees from Dalian University of Technology, China, in 1988 and 1991, respectively. She received her Ph.D. degree in modelling and optimization from Harbin Institute of Technology in 1999 and received her Ph.D. degree in virtual reality from University of Derby, UK, in 2002. She has taught and conducted researches at Harbin Institute of Technology, China, and De Montfort University, UK. She is currently a senior lecturer in simulation and virtual reality at Staffordshire University, UK.

Her research interests include modelling and simulation, virtual reality and augmented reality system, advanced interface, digital media, and distribution system.

Mansour Moniri received Ph.D. and B. Sc. degrees from Department of Electronics and Computer Science, Swansea University, UK, in 1993 and 1987, respectively. From 1987 to 1989 he worked with INMOS UK on developing transporter based testing systems for data converters. He was awarded R. O. Dunmore prize in electronic engineering in 1985. He was the director of Technology Research Institute until 2003, and he is currently faculty head of research at Staffordshire University, UK. He established research and enterprise centre in this area through government funding and provides consultancy to national and international companies.

His research interests include algorithmic development and implementation of signal, image and video processing systems.

Claude C. Chibelushi received the B.Eng. degree in electronics and telecommunications from the University of Zambia, Lusaka, Zambia, in 1987, the M. Sc. degree in microelectronics and computer engineering from the University of Surrey, Surrey, UK, in 1989, and the Ph.D. degree in electronic engineering from the University of Wales Swansea, Swansea, UK, in 1997. In 1997, he joined the Faculty of Computing, Engineering and Technology, at Staffordshire University Staffordshire University, Stafford, UK, where he is currently a reader in digital media processing. Before he joined Staffordshire University, he was a senior research assistant at the University of Wales Swansea from 1995 to 1996. He also worked as a lecturer in the Department of Electrical and Electronic Engineering at the University of Zambia from 1989 to 1991. He was a Beit Fellow from 1991 to 1995. He is currently a member of the Institution of Engineering and Technology.

His research interests include multimodal recognition, robust pattern recognition, medical image analysis, and image synthesis and animation.

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Yang, P., Wu, W., Moniri, M. et al. A sensor-based SLAM algorithm for camera tracking in virtual studio. Int. J. Autom. Comput. 5, 152–162 (2008). https://doi.org/10.1007/s11633-008-0152-6

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  • DOI: https://doi.org/10.1007/s11633-008-0152-6

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